OpenRaiser/Intern-Atlas huggingface dataset:
POS-tagging to highlight the skeletal structure of sentences
Venue: arXiv cs.CL 2024
Year: 2024

This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification. The model, fine-tuned on Russian text, demonstrating its effectiveness. The approach offers potential applications in enhancing natural language processing tasks, such as improving machine translation. Keywords: part of speech tagging, morphological analysis, natural language processing, BERT.
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Venue: arXiv cs.CL 2024
Year: 2024

Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on disciplines with standard answers, such as mathematics, physics, and coding -- which are well-suited for reinforcement learning (RL) -- but also places greater emphasis on open-ended resolutions. We aim to address the question: ''Can the o1 model effectively generalize to broader domains where clear standards are absent and rewards are challenging to quantify?'' Marco-o1 is powered by Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS), reflection mechanisms, and innovative reasoning strategies -- optimized for complex real-world problem-solving tasks.
Development of a threat modelling framework and a web-based threat modelling tool for micro businesses
Venue: arXiv cs.CR 2024
Year: 2024

While there is a plethora of cybersecurity and risk management frameworks for different target audiences and use cases, micro-businesses (MBs) are often overlooked. As the smallest business entities, MBs represent a special case with regard to cybersecurity for two reasons: (1) Having fewer than 10 employees, they tend to lack cybersecurity expertise. (2) Because of their low turnover, they usually have a limited budget for cybersecurity. As a result, MBs are often the victims of security breaches and cyber-attacks every year, as demonstrated by various studies. This calls for a non-technical, simple solution tailored specifically for MBs. To address this pressing need, the SEANCE Cybersecurity Framework was developed through a 7-step methodology: (1) A literature review was conducted to explore the current state of research and available frameworks and methodologies, (2) followed by a qualitative survey to identify the cybersecurity challenges faced by MBs. (3) After analyzing the results of the literature review and the survey, (4) the relevant aspects of existing frameworks and tools for MBs were identified and (5) a non-technical framework was developed. (6) A web-based tool was developed to facilitate the implementation of the framework and (7) another qualitative survey was conducted to gather feedback. The SEANCE Framework suggests considering possible vulnerabilities and cyber threats in six hierarchical layers: (1) Self, (2) Employees, (3) Assets, (4) Network, (5) Customers and (6) Environment, with the underlying idea of a vulnerability in an inner layer propagates to the outer layers and therefore needs to be prioritized.
Direct Speech-to-Speech Neural Machine Translation: A Survey
Venue: arXiv cs.CL 2024
Year: 2024

Speech-to-Speech Translation (S2ST) models transform speech from one language to another target language with the same linguistic information. S2ST is important for bridging the communication gap among communities and has diverse applications. In recent years, researchers have introduced direct S2ST models, which have the potential to translate speech without relying on intermediate text generation, have better decoding latency, and the ability to preserve paralinguistic and non-linguistic features. However, direct S2ST has yet to achieve quality performance for seamless communication and still lags behind the cascade models in terms of performance, especially in real-world translation. To the best of our knowledge, no comprehensive survey is available on the direct S2ST system, which beginners and advanced researchers can look upon for a quick survey. The present work provides a comprehensive review of direct S2ST models, data and application issues, and performance metrics. We critically analyze the models' performance over the benchmark datasets and provide research challenges and future directions.
Testing Uncertainty of Large Language Models for Physics Knowledge and Reasoning
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have gained significant popularity in recent years for their ability to answer questions in various fields. However, these models have a tendency to "hallucinate" their responses, making it challenging to evaluate their performance. A major challenge is determining how to assess the certainty of a model's predictions and how it correlates with accuracy. In this work, we introduce an analysis for evaluating the performance of popular open-source LLMs, as well as gpt-3.5 Turbo, on multiple choice physics questionnaires. We focus on the relationship between answer accuracy and variability in topics related to physics. Our findings suggest that most models provide accurate replies in cases where they are certain, but this is by far not a general behavior. The relationship between accuracy and uncertainty exposes a broad horizontal bell-shaped distribution. We report how the asymmetry between accuracy and uncertainty intensifies as the questions demand more logical reasoning of the LLM agent, while the same relationship remains sharp for knowledge retrieval tasks.
A Neural Network Training Method Based on Distributed PID Control
Venue: arXiv cs.LG 2024
Year: 2024

In the previous article, we introduced a neural network framework based on symmetric differential equations. This novel framework exhibits complete symmetry, endowing it with perfect mathematical properties. While we have examined some of the system's mathematical characteristics, a detailed discussion of the network training methodology has not yet been presented. Drawing on the principles of the traditional backpropagation algorithm, this study proposes an alternative training approach that utilizes differential equation signal propagation instead of chain rule derivation. This approach not only preserves the effectiveness of training but also offers enhanced biological interpretability. The foundation of this methodology lies in the system's reversibility, which stems from its inherent symmetry,a key aspect of our research. However, this method alone is insufficient for effective neural network training. To address this, we further introduce a distributed Proportional-Integral-Derivative (PID) control approach, emphasizing its implementation within a closed system. By incorporating this method, we achieved both faster training speeds and improved accuracy. This approach not only offers novel insights into neural network training but also extends the scope of research into control methodologies. To validate its effectiveness, we apply this method to the MNIST dataset, demonstrating its practical utility.
Popular LLMs Amplify Race and Gender Disparities in Human Mobility
Venue: arXiv cs.CL 2024
Year: 2024

As large language models (LLMs) are increasingly applied in areas influencing societal outcomes, it is critical to understand their tendency to perpetuate and amplify biases. This study investigates whether LLMs exhibit biases in predicting human mobility -- a fundamental human behavior -- based on race and gender. Using three prominent LLMs -- GPT-4, Gemini, and Claude -- we analyzed their predictions of visitations to points of interest (POIs) for individuals, relying on prompts that included names with and without explicit demographic details. We find that LLMs frequently reflect and amplify existing societal biases. Specifically, predictions for minority groups were disproportionately skewed, with these individuals being significantly less likely to be associated with wealth-related points of interest (POIs). Gender biases were also evident, as female individuals were consistently linked to fewer career-related POIs compared to their male counterparts. These biased associations suggest that LLMs not only mirror but also exacerbate societal stereotypes, particularly in contexts involving race and gender.
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language
Venue: arXiv cs.CL 2024
Year: 2024

Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particularly on the resource-constrained Urdu language, which is widely spoken across South Asian nations. It outlines current research trends, technological advancements, and potential directions for future studies in Urdu ASR, aiming to pave the way for forthcoming researchers interested in this domain. By leveraging contemporary technologies, analyzing existing datasets, and evaluating effective algorithms and tools, the paper seeks to shed light on the unique challenges and opportunities associated with Urdu language processing and its integration into the broader field of speech research.
Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods
Venue: arXiv cs.CR 2024
Year: 2024

A distributed denial-of-service (DDoS) attack is an attempt to produce humongous traffic within a network by overwhelming a targeted server or its neighboring infrastructure with a flood of service requests ceaselessly coming from multiple remotely controlled malware-infected computers or network-connected devices. Thus, exploring DDoS attacks by recognizing their functionalities and differentiating them from normal traffic services are the primary concerns of network security issues particularly for online businesses. In modern networks, most DDoS attacks occur in the network and application layer including HTTP flood, UDP flood, SIDDOS, SMURF, SNMP flood, IP NULL, etc. The goal of this paper is to detect DDoS attacks from all service requests and classify them according to DDoS classes. In this regard, a standard dataset is collected from the internet which contains several network-related attributes and their corresponding DDoS attack class name. Two(2) different machine learning approaches, SVM and Logistic Regression, are implemented in the dataset for detecting and classifying DDoS attacks, and a comparative study is accomplished among them in terms of accuracy, precision, and recall rates. Logistic Regression and SVM both achieve 98.65% classification accuracy which is the highest achieved accuracy among other previous experiments with the same dataset.
Performance Analysis of Traditional and Network Coded Transmission in Infrastructure-less Multi-hop Wireless Networks
Venue: arXiv cs.NI 2024
Year: 2024

Infrastructure-less Multi-hop Wireless Networks are the backbone for mission critical communications such as in disaster and battlefield scenarios. However, interference signals in the wireless channel cause losses to transmission in wireless networks resulting in a reduced network throughput and making efficient transmission very challenging. Therefore, techniques to overcome interference and increase transmission efficiency have been a hot area of research for decades. In this paper two methods for transmitting data through infrastructure-less multi hop wireless networks, Traditional (TR) and Network Coded (NC) transmission are thoroughly examined for scenarios having one or two communication streams in a network. The study has developed network models in MATLAB for each transmission technique and scenario. The simulation results showed that the NC transmission method yielded a better throughput under the same network settings and physical interference. Furthermore, the impact of increasing numbers of hops between source and destination on the network capacity and the communications latency was also observed and conclusions were drawn.
Optimal Transcoding Preset Selection for Live Video Streaming
Venue: arXiv cs.MM 2024
Year: 2024

In today's digital landscape, video content dominates internet traffic, underscoring the need for efficient video processing to support seamless live streaming experiences on platforms like YouTube Live, Twitch, and Facebook Live. This paper introduces a comprehensive framework designed to optimize video transcoding parameters, with a specific focus on preset and bitrate selection to minimize distortion while respecting constraints on bitrate and transcoding time. The framework comprises three main steps: feature extraction, prediction, and optimization. It leverages extracted features to predict transcoding time and rate-distortion, employing both supervised and unsupervised methods. By utilizing integer linear programming, it identifies the optimal sequence of presets and bitrates for video segments, ensuring real-time application feasibility under set constraints. The results demonstrate the framework's effectiveness in enhancing video quality for live streaming, maintaining high standards of video delivery while managing computational resources efficiently. This optimization approach meets the evolving demands of video delivery by offering a solution for real-time transcoding optimization. Evaluation using the User Generated Content dataset showed an average PSNR improvement of 1.5 dB over the default Twitch configuration, highlighting significant PSNR gains. Additionally, subsequent experiments demonstrated a BD-rate reduction of -49.60%, reinforcing the framework's superior performance over Twitch's default configuration.
Generative AI for Music and Audio
Venue: arXiv cs.SD 2024
Year: 2024

Generative AI has been transforming the way we interact with technology and consume content. In the next decade, AI technology will reshape how we create audio content in various media, including music, theater, films, games, podcasts, and short videos. In this dissertation, I introduce the three main directions of my research centered around generative AI for music and audio: 1) multitrack music generation, 2) assistive music creation tools, and 3) multimodal learning for audio and music. Through my research, I aim to answer the following two fundamental questions: 1) How can AI help professionals or amateurs create music and audio content? 2) Can AI learn to create music in a way similar to how humans learn music? My long-term goal is to lower the barrier of entry for music composition and democratize audio content creation
An Investigation of the Relationship Between Crime Rate and Police Compensation
Venue: arXiv cs.CY 2024
Year: 2024

The goal of this paper is to assess whether there is any correlation between police salaries and crime rates. Using public data sources that contain Baltimore Crime Rates and Baltimore Police Department (BPD) salary information from 2011 to 2021, our research uses a variety of techniques to capture and measure any correlation between the two. Based on that correlation, the paper then uses established social theories to make recommendations on how this data can potentially be used by State Leadership. Our initial results show a negative correlation between salary/compensation levels and crime rates.
Multiverse of Greatness: Generating Story Branches with LLMs
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents Dynamic Context Prompting/Programming (DCP/P), a novel framework for interacting with LLMs to generate graph-based content with a dynamic context window history. While there is an existing study utilizing LLMs to generate a visual novel game, the previous study involved a manual process of output extraction and did not provide flexibility in generating a longer, coherent story. We evaluate DCP/P against our baseline, which does not provide context history to an LLM and only relies on the initial story data. Through objective evaluation, we show that simply providing the LLM with a summary leads to a subpar story compared to additionally providing the LLM with the proper context of the story. We also provide an extensive qualitative analysis and discussion. We qualitatively examine the quality of the objectively best-performing generated game from each approach. In addition, we examine biases in word choices and word sentiment of the generated content. We find a consistent observation with previous studies that LLMs are biased towards certain words, even with a different LLM family. Finally, we provide a comprehensive discussion on opportunities for future studies.
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational and memory demands, due to billions of parameters, hinder deployment in resource-constrained environments. A promising solution is knowledge distillation, where LLMs transfer reasoning capabilities to Small Language Models (SLMs, $\le$ 1B parameters), enabling wider deployment on low-resource devices. Existing methods primarily focus on generating high-quality reasoning rationales for distillation datasets but often neglect the critical role of data quantity and quality. To address these challenges, we propose a Feedback-Driven Distillation (FDD) framework to enhance SLMs' mathematical reasoning capabilities. In the initialization stage, a distillation dataset is constructed by prompting LLMs to pair mathematical problems with corresponding reasoning rationales. We classify problems into easy and hard categories based on SLM performance. For easy problems, LLMs generate more complex variations, while for hard problems, new questions of similar complexity are synthesized. In addition, we propose a multi-round distillation paradigm to iteratively enrich the distillation datasets, thereby progressively improving the mathematical reasoning abilities of SLMs. Experimental results demonstrate that our method can make SLMs achieve SOTA mathematical reasoning performance.
A Lightweight Edge-CNN-Transformer Model for Detecting Coordinated Cyber and Digital Twin Attacks in Cooperative Smart Farming
Venue: arXiv cs.CR 2024
Year: 2024

The agriculture sector is increasingly adopting innovative technologies to meet the growing food demands of the global population. To optimize resource utilization and minimize crop losses, farmers are joining cooperatives to share their data and resources among member farms. However, while farmers benefit from this data sharing and interconnection, it exposes them to cybersecurity threats and privacy concerns. A cyberattack on one farm can have widespread consequences, affecting the targeted farm as well as all member farms within a cooperative. In this research, we address existing gaps by proposing a novel and secure architecture for Cooperative Smart Farming (CSF). First, we highlight the role of edge-based DTs in enhancing the efficiency and resilience of agricultural operations. To validate this, we develop a test environment for CSF, implementing various cyberattacks on both the DTs and their physical counterparts using different attack vectors. We collect two smart farming network datasets to identify potential threats. After identifying these threats, we focus on preventing the transmission of malicious data from compromised farms to the central cloud server. To achieve this, we propose a CNN-Transformer-based network anomaly detection model, specifically designed for deployment at the edge. As a proof of concept, we implement this model and evaluate its performance by varying the number of encoder layers. Additionally, we apply Post-Quantization to compress the model and demonstrate the impact of compression on its performance in edge environments. Finally, we compare the model's performance with traditional machine learning approaches to assess its overall effectiveness.
IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search
Venue: arXiv cs.IR 2024
Year: 2024

The Interactive Knowledge Assistant Track (iKAT) 2024 focuses on advancing conversational assistants, able to adapt their interaction and responses from personalized user knowledge. The track incorporates a Personal Textual Knowledge Base (PTKB) alongside Conversational AI tasks, such as passage ranking and response generation. Query Rewrite being an effective approach for resolving conversational context, we explore Large Language Models (LLMs), as query rewriters. Specifically, our submitted runs explore multi-aspect query generation using the MQ4CS framework, which we further enhance with Learned Sparse Retrieval via the SPLADE architecture, coupled with robust cross-encoder models. We also propose an alternative to the previous interleaving strategy, aggregating multiple aspects during the reranking phase. Our findings indicate that multi-aspect query generation is effective in enhancing performance when integrated with advanced retrieval and reranking models. Our results also lead the way for better personalization in Conversational Search, relying on LLMs to integrate personalization within query rewrite, and outperforming human rewrite performance.
TEXGen: a Generative Diffusion Model for Mesh Textures
Venue: arXiv cs.CV 2024
Year: 2024

While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of relying on pre-trained 2D diffusion models for test-time optimization of 3D textures. Instead, we focus on the fundamental problem of learning in the UV texture space itself. For the first time, we train a large diffusion model capable of directly generating high-resolution texture maps in a feed-forward manner. To facilitate efficient learning in high-resolution UV spaces, we propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds. Leveraging this architectural design, we train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images. Once trained, our model naturally supports various extended applications, including text-guided texture inpainting, sparse-view texture completion, and text-driven texture synthesis. Project page is at http://cvmi-lab.github.io/TEXGen/.